{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import sys\n",
    "sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from agots.multivariate_generators.multivariate_data_generator import MultivariateDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.985941</td>\n",
       "      <td>-0.144821</td>\n",
       "      <td>-0.186724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.985941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.129324</td>\n",
       "      <td>-0.187107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>-0.144821</td>\n",
       "      <td>-0.129324</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x3</th>\n",
       "      <td>-0.186724</td>\n",
       "      <td>-0.187107</td>\n",
       "      <td>0.197286</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x0        x1        x2        x3\n",
       "x0  1.000000  0.985941 -0.144821 -0.186724\n",
       "x1  0.985941  1.000000 -0.129324 -0.187107\n",
       "x2 -0.144821 -0.129324  1.000000  0.197286\n",
       "x3 -0.186724 -0.187107  0.197286  1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1337)\n",
    "\n",
    "STREAM_LENGTH = 200\n",
    "N = 4\n",
    "K = 2\n",
    "\n",
    "dg = MultivariateDataGenerator(STREAM_LENGTH, N, K)\n",
    "df = dg.generate_baseline(initial_value_min=-4, initial_value_max=4)\n",
    "\n",
    "for col in df.columns:\n",
    "    plt.plot(df[col], label=col)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EvUnF2V5r+dooJ20l6VqZL6jh6+SkrWRL9CYVN0cdIDN8SbKG6oCvyElbycYYTCpujnbVX0uSdG3Uyl8jrUZO2ko2xmBScbHXoihylY4kWYOpMuJrZbdMydYYTCr2Og32Wo3M8CXJCqrq9ZoLM3xZw5dsgd5kxl6nwUGnkRm+JFlB1VSYpYYvM3zJhhjMKg46LfY6rQz4kmQF5ouutJUBX7IhBpOKvdaS4cuSju06lnOMF7a/QH5Ffm0PRbqCiwJ+1Q1Q6mrAVxTla0VRshRFOXbBY96KomxUFOVU5f+9rHEs6dpV1fAtJR25Dv+fyi7L5tczv2JUjdWPFVQUEJ8Xj9Fs/JtX/smkmsiryLvs8/F58Ty68VHWnV3HJ4c+QRUqZwvPXvPYpeujuj2yhqvqlimEYO3RDNIKym/I+KpYK8P/Bhj4l8deAjYJIRoBmyq/l2xA9aStzPD/sc8Pf86gFYN46Y+XeHzj42SWZrI/cz9DVw5l1OpR3Lb4Nh7d+CjbUrYBsDVlK/OPzr9oH9ll2UxcP5H+P/bncPbhS46xPXU7D/72II5aR/rX78+PCT8yaeMkhq0cxrqz6644xgXHFjBkxRA2Jm9E2ODEYV30Z4avqQ74+WUGTGaVE+lFl2T7n249zRPfH+COuTv4fm8yU5cfZvXh9Os+Tp01diKE2K4oSvhfHr4T6Fn59UJgK/CiNY4nXRv9hQG/jl54VWYsI+Z8DN1CuqFRrJPX/HL6Fz459An96vejnX87/hv7X/r92A+AcPdwprafSkJ+ApvObeKZrc8wu+dsXtz+ImWmMiLcI6jvXp/v4r7j93O/YzAb8HTw5OnNTzOn9xxa+rYkrTiNeUfnsTJxJU29m/LR7R/hYu/C7vTdxGbG4u/sz+zY2fQO642D1oHU4lQMZgMNPBtUj7HIUMS8I/MwmA08u/VZhkcO5/XOr2OnsbPKZ3AlB7MO0sCjAR4OHjfkeLWtuMKIm6PdRe2RAz0ccXXQ8czSQ0xbeYziChNjO9ZjxoiWlBnMfLH9DHM2naJ/8wASs0p49edjuDnqaBLodt3Ha5WAfxkBQogMACFEhqIo/jVtpCjKJGASQFhY2HUcjlTFYFJxqCrpGOtWwBdC8OvZX5kVM4us8ize6fYOwxoO+1f7SilKYXPKZiY0n0BSURJv73mb9gHteb/H++g0OjoGdSQmM4ZyUzmjGo+qDnKPtHyEkb+M5MnNT+KodSTCI4Lpu6dTbipHq2jpHtqdSa0moVN0jFs3jnFrx6FTdJiECTuNHfdH3c/kNpNx1DkC8GX/L9EoGgoNhTyy4RFm7ptJpGcksw/MRqfoWDl8Jf7Oll+vJSeXUGIsYcmQJWxJ2cIXR74gvyKfOb3nXPSHr8hQxLaUbQyOGIxWo72qz9WgGnDQOlz2+flH5zPn4Bx61evFnN5z/tVnfjOZs+kUH21M4I7WwbQL8wRAo4EgDye2Te3Jgp1JZBVXAPDDvhTO5pQSl1FMYbmRoa2C+Gh0GwxmlVPni4kK9sBed/2nVK9nwL8qQoh5wDyA9u3by/PPG0Bv/jPDr7BSwK8wVWCvta8xm9ab9exI3UGPej2ua6Z5puAM03dP52DWQaJ8onDQObDoxCLuaHAHRYYiXv7jZTJKM3iq7VP0qtcLpXJyrSZm1cwL21/gWO4xWvu1Zs2ZNahCZWaPmeg0ll+bxl6NaezV+JLXejp68mbXN5m8aTJPtX2K6IBoxq0bR/uA9rzb/V18nXyrt107Yi3bUreRWJBIsGswPUJ7EOIactH+onyjqr8eHDGY5QnLAWjj14a4vDje2fMOs3vN5nzZeb478R3dQ7oT5RtFlG8Ung6ezNw/kx8TfmR0k9HV+5kdO5vlCctJLEhkSvSUv/1cP4r5iCXxSyg3lTO712za+LVh7K9jmdphKn3D+vLtiW/59cyvxOXFEeIawpaULZzMO0lT76Z/u9+biRCCFQfSWHMknbM5pYT7urA1Ppt2YZ5sPJFZXY5xqAzaPq4OPD+gSfVrhYAt8dn0burPuE71ia5vmdK012loG3bjpjcVa9X4Kks6a4QQLSq/jwd6Vmb3QcBWIUSTv9tH+/btRUxMjFXGI9VMCEHEy2t5unckx9KLyCquYM1T3a/qtYX6QsasGUOIWwhDIobQt35fHLWOLEtYxuzY2Tzc8mEebf3oJa+be3Au847Mo2tIV6a0m0KFuYKWvi2vqtSSV2rgtVXHyC3R4+1iT4tgD+5uXw8/N0umGZ8Xz/8O/Q8Pew/WJ63HWefMlOgp3Bl5Jz+f+pnpu6czuc1kViWu4nzZeYJcgjhXfI7XOr12UQD8q0UnFvH+/vfRKBqGNRzGlpQtdA7qzAe3f3BVnxVYJnI9HT2rv3Z3cL/m8pIQgvNl58kozaClb0u+PfEts2Jn0SesDyfzTlKoL+Sbgd/QxPvPYPPwhoeJy4tj+R3LCXENIbM0k0ErBuFu705eRR4vdHiBe5veW2Omv/r0al7Z8Qp9wvoQlxuHr7MvXYK78Pnhz4n0jOT/2v0fT21+ihY+LRjcYDDDGg5j0E+D6BjUkdm9Zl/Te7UWIcTf/nH/O4lZJeSVGvgpNpWlMSmE+zgT6e/GwXP5dGrow8f3tKGg3EhMUj65pXruaV8PnfbGL35UFCVWCNH+ittdx4D/AZArhHhPUZSXAG8hxAt/tw8Z8K8/vclMk2nreb5/Y46lFXEmp4QNU26/qtcuObmEd/a+Q5BLEBmlGeg0OlShogoVJ50T7vbu/HbXbxcFjkJ9IQN+GkCAcwDJRcmYhWVV0IDwAbzd9e3qskVNknNLeXhhDOfyymgd6kla2RkK3L7A2dyclWP+y4HkAhYmPc+5ktPYa+1p69+WNzq/UZ1BV5gq6P9jf/L1+YS5hfFOt3do4duCxzY+xom8E6wevhq9Wc8H+z/gbOFZpneZThv/NqSVpDFi1Qg6BHbA3d6dNWfWAPBJn0/oEdrj337014VJNfHZ4c9YcnIJAPP6zbvojADgbOFZ7vrlLoyqkfru9XG3dycuL45Vd65ixr4Z7EzbSTPvZnzS5xP8nP2qX5danMrIX0YS5RPFl/2/ZHnCcmbsnYGTzgknnRN5FXm427vj6eDJyuErq8/eZsXO4utjX7Pn3j242LncuA/jAoVlRjyc7Vh/LIOpPx7BxV7HgKgAXh3SHHudBiEEJzOLifB1wdHO8u+1RG9iw/FMYpPzEVj+/e1MzK3e51O9I5nStzEazb/743E9XW3At0pJR1GUH7BM0PoqipIKvAG8ByxTFOUh4BxwtzWOJV2bqlU5f12lYzQb2Z+5n9uCbrtsTXf1mdU08mrET3f8xNGco2w6twl7rT3NvZujV/VM3TaV/ef30ymoU/VrFh5fSKmxlA9u/wCj2UhSURIpxSl8euhTNiRtwMXOhdb+rRkROYLWXrfz8aYEDqcU4mSv5cC5fFzsdSx8sCMeHlk8vOFTXMxmys076fv9A5jLQnHwP8bUdq8xoeWl2bqjzpEPbv+AxLxUjsU3YOkOLZmNc3il0yvc9ctd3LPmHnLLc7HT2uFu78796+/n0VaPcjjnMAoK026bRlJREmvOrMHb0ZvOwZ2vw0/k2ug0Op5q+xSPtHwEvVlf42RphEcEy4Yu44+0P9ibuZe9GXsZ1WgUYe5hfNbnM9YnreeNXW/w5OYnWTBgAc52zgB8dvgzVKHybvd30Wl03NnwTuYenEuxoZiPe33MG7veIKM0g9c6vXZRqa6eWz0Aig3FtRLw/7f5FB9uSKBvswC2n8qmkb8r9X2cWbg7mfjzxXRq4MOG4+c5kVGEt4s993cJZ2S7EB75Npa4jCLcHHXYazU42ml5aVBTooLd8XaxJyr45p+IttYqnbGXeaqPNfYvWU9VgHfQaatbK5hVMy/veJnfkn7jsdaPMbnN5Etel1SYxJHsIzwX/RyKotDKrxWt/FpVP68363Gzc+PHhB8RQuBq50pcXhxfHfuKQeGDqmvdVdlnW/+27MvcR6G+kF3pu3h+2/Ooef0wFrWkfWgE5XoNk3tGcl+nMDxd4O7VU3HSObF06Dd8dWANPyV9gc41DqEPYcmWAAI1mWgUhVNZJTjoNHRu6EOzIHda+bRnxgoTcemZODtoWbL/HJ/dF83/tf0/1ietZ2iDodzT5B5c7V2ZsXcGnx7+FICXOr5EkGsQ/s7+NPBoQN/6fWucf1BVgVG1XLlcmxx1jn97thTpFUmkVyQPtHiger4FQFEUBkUMwlnnzNNbnublP15mVq9ZJBVa/tBNaD6BQJdAAJztnHms1WMcyj5Ep6BOPN/+ef5I+4P+4f0vOparnSsAJYYSsHK8N5pV1h7NYMm+FHRahfo+zuiNKiPahtAl0pdl+1P4cEMC7cI82ZGYTainE4seug1vF3t+jE3l1Z+PsudMHhG+Lrw2tDm7EnP4aGMCs35PwF6rYd74aPo2C7DJLN4arFbSsQZZ0rn+MgrL6fzuZt4d2ZIDKalsSP+edo0sSxgbeDQgqSiJ+f3n0yGwQ/VrDmcf5rNDn7E7YzcbR22sXhHyV//Z/R9+TPjxose6h3Tnw9s/rM4aL7QlPovpvxyneyMvVqd/jOpi+dl7O3qzeMji6snLGXtn8MPJH/iq/1d0DOoIWLLHHWk7KCoIZMYv2RSWX3zBk06jMKFzOPuScjmeXsQX46Lp0diPMfP2cDKziJl3tWJY6+BLarvrzq7jeM5xpkRPqT7T2RafRZiPCxG+luilqoINJ84zZ9MpTmQUoVGgd1N/nugVSbsbOAFnbd/Hfc97+97jjgZ3kJCfQGpJKutGrsPL8Z+9p+2p25m8aTLfDf6O1n6t/9VYjGaVRbuTmbf9DE/0asiEzuFsic/irTUnOJNdSoSvC872WtIKyjGbBQazygNdI/hi+2m6Rfry9f0dKKkwodMq1a3Aq/aruaDfDUBscj5f7TjDfbfVp2ukb03DsXk3vIZvDTLgX3/JuaXc/sFW/nt3a346/R1Hy7+noUdDhjYcytimY7l79d2klaTRN6wvHQI7sDt9N5tTNuOkc+KhFg/VOClb5XzpedaeXUsT7yaUGcsoNhQztOHQGjPjH2NTefGnIwS6O3K+qAIne4VXRyk4OeqZuW8mQa5BLBq0iOSiZEavGc19ze7jpY41X7tnNKvsT8pDqyg0D3anzGDmzdUn+PVoBhG+LjzTtxF3trH88cgu1vPQwv0cSS3Ew8kOVRU83L0BT/eJvCT4l+pNvLbyGCsOpuGg0/BgtwgqjGY2HD9PWkE5DfxcGNoyiAqTyooDqRRXmPj6/g43bdAQQjBj7wyWxC8hwDmAlzu+TJ/6//wkPfZ8LPevv58v+n1Bl+Aul92uzGDC2f7iIoPBpPLp1kQW7z1HVrEefzcHskv0dIv05Y9TOUT4uvDyoKYXZeG5JXpGfb6bszmldIv05csJ7XGyr90zrhtNBnypRgnni+k/aztzx7bl4+PPklGSy/FJG6ufzynP4dvj37IicQWF+kKcdE480vIR7m12r1XqsUIIPtt2mvfXx9Mt0pfPx0dTXGHErApCvSxnATvTdvLEpifoXa835eZyjmYfZd1d63C3d/9Hx6kKGH8N5KoqWHEwjdjkfLKKKth0MosO4V74ujrQs4kfI9qGolHgwYUx7DiVzRM9IzmZWcTvcVk46DR0jPBmdPt6DGoRWL0iI6/UwNh5e0jOK2V0+3qM6RBG82D3i47504FUErNLmNwrEnfHG3Mh1D9lVs3Eno+ljX+b6rLPP3Uy7yR3r76bWT1n0bd+3xqOIZi5/iTz/zjDa0ObM7ZjGHvP5hHi6ch76+L5Pe48PZv4cX+XcDo18GHc/L0cSingiV6RPNkrssb16mkF5aw5nM7ELuHVk7C3EhnwpRodSytk6NwdzB7blNcP3UVFTjfin517SVCsWv7npHOy2lWTOSV6Xl91jLVHMxnWOpgP72592YtNFh5fyIcxHwIwJXoKD7Z40Cpj+CshBJ9vO8PKg2mUGkyk5pfj5+ZAfW9nYpLzeXdkS8Z2DEMIQX6ZEU8nu8vWd7OL9Uz/5TibTp5Hoygsf6wzzYPc+eNUDh9vOkVssqUJWrCHIx/c3fqmPRO4kpSiFAb/PJhXO/w0byvGAAAgAElEQVSHkY2HY6/TcDanlB2nsjmWVkRMch6nK8syZ3NK8XaxJ6/UUP36t+6MYnzn8Orv9SYzeaUGgjycauHd3Bxu6Cod6eZR1Q45qfQgAjPmkqboTeolWZGiKNWTdf9GTomezXFZuDjo2H0mh3VHM8ktNaDTKLw4sCmP9mjwtxNjE5pPIKU4hZjMGMY2vdyagGunKAqP92zI4z0bIoRga0I2y/ansONUDo/2aMDYjmHV23m7/H3G6+fmwCf3tSOrqII7P9nJ/Qv242Sn5VxeGf5uDsy8qyWR/m5MXX6Y++bv5e7oUAa3CiLU0wlfVwe8rrD/+MxiZm1MwKSqPN6zIdH1va/6fZpVcVHd+nqqmq95Z91Bvlrvw13RoXy0MQGDScXL2Y4WIR5M7hXJHa2DeXnFUc4XVTCuU33ySg2EejnRvZHfRftz0GllsLcSmeHfYnafzmXsl3sYcPtWYnL+IPf4KxyZPuiqSwzpBeX4uznUeHFJhdHMjlM5nMkp4dOtpykos0yk2us0DIgKJCrYnZ5N/GgaePWlGVWoVuuFcyMdTy/kkYUxNApwY2irIIa1Ca5eyVNuMPPBb/F8tye5upeRndbyh/DBrhEYzCr/WX2cU+dLCPZ04qVBTdmflMeUpYdwddBhp9WQW2qoPvu4krM5pYyZt5s+zQJ4Z3iLf30R0tVIzCph9ZEkvk4bi2PxUERBH3JK9HRp6MN7I1tRz9vpuh7/ViUzfAmAZfHLAAhxDWFH2g5S8ktxDD7LrqxDNHfrRS7aSzpmbonP4s3VJ+jd1J9WoR6cSC+if1QgJzOLmLbyGBE+LozpWA97rYaBLYII9HCkqMLIgwv2E1NZtmgX5skbd0Sh0yqEeDrh6fzv6sE3Y7AHiAr2YNfLNU94Otlref2O5kwd0IQD5/LJKzWw+nA6b/8ax4+xqdjrNBxNK6RDfW82xZ1n1+lcCsoMdAj35vNx0TjYaXjsuwO8tvIY9b2diQ734vWVxzmcWsDo9vVoG+ZJAz9XPJzsyCnRc/+CfeSXGlm89xx+rg4807fRvw66ZlVgMKmXTIpWGM18uvU0/9t8ClUI3JpqGBHtyyMturMzMYehrYJq5QpU6WIyw6/DzhSe4c6Vd1Z/76h1RAiFCpORYRH30NRxOK+vPM3Ol3oT4umEyayycHcyM9bGEeDmwPliPWZVoChQ9c+kS0Mf8koNnMwsBsDfzYGn+jRi0e4kzuaU8u7IVnSN9CHQ3VFmcv9AVa+WBbvOknC+hFmj2zCkVRAnM4t4cMF+vFzs+WFSp+ozscJyIyM+3cmZ7FIC3B04X6SnSYAb8ectPxdney1P9GzI0pgUsor0LH6kE4v3nuOnA6kMahHIW8Nb4OtacyO0y4nPLOb/lhzkfFEFn9zXji4Nfckt0fP88sPsTMzFYFYZ2S6ElwY2ZcSvfRjSYAiv3PaK1T8r6VIyw5f4JfEXtIqW+f3nU24qJzogmk0n8nnqh1gmDurFiYxCAPRGMyczi3hmySFOZhbTu6k/c8a2paDMQF6pgQhfFxbuSqKw3MgLA5ui0yjklxlJyy/nse9ieW3lMcK8nZk/sQO3N/a7wqikmiiKwl3RodwVHUqF0Vw9p9I00J3Nz/dEq1GwuyBD9nCy48fHurB4bzJ/nMrhtaHNGdoqmNPZJSTllPLNriQ+3JBAgLsDSyZ1om2YF23qeRLp78qsjQnsOLWVyb0jmdT97+dS8ksNfLzpFJtPZpGSX4aPiz1eLvaM/2ofw1oHcyS1gLSCciZ2qU+vpv50aWiZiHaxc6HUWHp9PzTpH5MBvw6pOltTFAWzambFqVWYShozbwN8el9XdFoNRnMeYLnKtmoi7InvD5CSV4azg47P7mvHwBaBKIqCq4Oueqnkk70bXXQsbxd7vF3s+eXJrhxNK6R7I78bNilY1/11Av1yywy9Xex5sneji342Df1caejnSu+m/myNz6ZlqEd1Jq/VWCao+zUP4N21cby37iQZBeVMHxZV49nY9oRsnvrhIMUVRvo1D2BUdChjO4bhaKfh/fXxrDqUhipg4QMdua2Bz0WvlQHfNsmSTh1gUk18H/c9yxOWk1eRx4TmE8gpKWdp4tcoWRMpym1G76b+BHs6Vq+WiZnWFx8Xe9YcyeC9dSfxdXPgi3HRBHpc/vJ8qe4QQjBjbRxf/nGWliEeRPi6MLRVEL2b+qPVKHy3J5npq0/QyN+Vj8e0rfHmHHqTmQqDiofzpRP+9629D2edM1/2//JGvJ1bnizp3AS2pWxj1elVTG0/lSDXoEueLzeVU2IowdfJF0VRyC3P5dsT33Jfs/uq2xsUGYp4YdsL7EzfSTv/doS51eeTQ59YdmAIZOm4B9h4Io+5m0/hqNPSMcKbR3o0qM767mgdzJCWlmPX1f4h0qUUReGVwc3wdLZnZ2IOu07n8MvhdALcHWjo58qu07n0auLH3Hvb4epQc5iw9GOq+ezDRSczfFskA34t2ZW+iylbp2BUjcSej6VbSDdSilNILU4lOiCa0U1GM3XbVHIrcvFy8GJC1ATWn11PfH48sedj+fD2D1l4fCErE1dSYapgeufpDIkYznPLD1OS2JEekQG8e3cPgj2daRbkw1O9L20dUEUG+luToihM7hXJ5F6RmMwqm09m8cO+c8Qk5/PCwCY81qPhv/634WLnQlZZlpVHLF0rWdK5wYyqka+Pfs3nRz6noUdDpnWaxlt73qJAX0CYWxi+Tr78nvw7JmEi0CWQ8c3GsztjNzvSdmCnsWNM0zEsOrEIjaJBg4YBEQMY22Qc2486sHB3ErmlBl4e1JRHujeQq2SkWvPqjlfZl7mPjaM2Xnlj6ZrJko4NyirL4rmtz3Eo+xADwwfy6m2v4unoyU/Dfrpou0NZh/g58Wceb/04gS6BTIiawL6MfQihpayoHsX17ckzJfNc+6coKfHm1aXHOJpWSK8mfjx2e8NLJtAk6UaTk7a2SQb8G2B76na+PPIl8fnxAMzsPpPBDQZfdvs2/m1o498GsDTd2hh3ni0nHfn1aAbFFZlAI6ARW7cnUGow4+6oY974aPpH/ftWCJJkTa52rpQZy67p9oKS9cmAD+RV5DH34FxO5p5kTNMxDGkwpPpG1dcqPi+e57c9j5+TH8MaDmNMkzFEekVe1WtL9CaeWXKI3+PO4+ago08zf0a2C0VRLJewn8oqoXWoB/2bB16xD4sk3UjOds6YhRm9Wf+3N2aRbqxbJuCnFKfw35j/4u3oTe+w3nQN7sqXR79k6cml5FTkoEFDqFso03ZO4/u473mt02u09Gt5xf0uOLaADUkb0Gl0zOg2g3rultu7pZWk8euZX1kWvww3OzcWDlpYfa/VK6kwmlkWk8IX286QWVTB60ObM75z/YsuvPlrg6nrQdXrEUYTWtfauS+pdPOqaqVdYiyRAd+G3BIBP6U4hYd+e4giQxEKCssTlhPuHk5SURLdQrox0nckg8IHEeERwW9Jv/HB/g+4d+299KzXk+einyPcIxyA3PJcdqXvYmD4QOy0dmw+t5mPYj+iuU9zjuUeY/HJxUztMJXPDn/G10e/xqAaiPKJ4rVOr11VsFdVwdc7z/LZ1tPklhpoF+bJR6NbX5eafPGmTej8A3Bq2eKy22S+/gblhw8T8csqNPbyDEK6elUBv8xYBrLRpc2oEwHfaFY5l1dGUk4pkf6u1Pf5MyOtCvalxlIWDFhApFckP8T9wJdHv+TRVo8yuc3ki2qMAyMG0i2kG4viFrHoxCIe3vAw3w3+jj/S/mB27GyKDEWsTFzJ0AZD+Sj2I5p5N2PRoEVM2zGNX07/QpBLEJ8f/pxB4YN4tv2zV91iuLDMyDNLD7IlPpvujXx5qncjOoR7XZf6pxCCjFen4di8OWFff3XZ7cqPHcOQlET+ou/weej69KOX6iYXneV3UE7c2pY6EfDXHElnytLDgKUV7//1aYTOvpA8ZS+b0lZQbipnfv/5NPNpBsCEqAmMbz7+ssHU1d6Vx1s/Tq96vZi4biKDVgzCpJpo59+OXvV68fHBj9mXuY96bvWY2WMm9lp7RjUexbqkdXwY8yG3Bd7GzB4zrzpYZxVVMOHrfZzOLuGt4S0Yd1vYdZ3oMufkYC4ooPzoUYSqYkhKRrG3xz40pHobYTRiSE4GRSHns8/QuLni1LoNjk0aX7dxSXVHVU98GfBtS50I+B0jfPjv3a0J8XLiy+1n+O/WbTiFfYlGV0p910bM7T+3OthXuZqA2tS7KR/1/Ij5R+dzX7P76BPWB0VR6FmvJ8WGYlr4/tlbvENgB8LcwsgozeDVTq9edcDOKdFzz7w9nC+q4JsHOt6QuyDpExMBUIuLMZw9S8qkR1FLSwlfugT7+vUBMKSkgsmEzyOPULBsGZmvv4EuMJBGW7dc9/Gp5eXkfPIJhuRz2IXVI2Dq1Ot+TMm6qks6prJaHol0oToR8EM8nRjYyovntz1Psssp/BqX4qh1wpT2NOnn/CiNDgAfMJnVf9yTu2tIV7qGdL3osaqa/oUUReGdbu9QoC8gwiPiqvZddUPtjMJyvn/4tn90B6NroT+VWP11wbJlGNPSQFFIefQxwpf8gNbTE8PZMwC49euL71NPkjvvS3L+9z9M+fnovLyu29jUigpSnniCsj17sQsKonjjRtx69cK5/RWvKZFsSFXAlxm+bakTdyQoM5bxxO9PsDt9N23829ApqCPfDPqaRROG4WSn5e4vdtPrw600mraO0V/sZt3RDP7pFcZCiCu+po1/G3rW63nFfRWUGRj/1V46zvid4+lFfHJvuxsW7MGS4Ws8PNC4uJC/+AdQFELmfIwxLY3Up55GGAzoz1gCvn1EBBp7e5xatwLAkJh4yf6EECSPG0/Bzyv/1XjKDhygeNMmALLef5+yPXsJfu9dGvy6Bq23NzlfzKNkx06yZs1GLbtyxiiEoGjdOlL/7xlMubn/akzStbHlgG9STaw5s4bHNj7G9F3T2XJuyz+OB39HCMGJ3BOcLjjNohOLGPDjAH4+9bPV9n8t6kSGvyF5A4eyDzGz+0wGRgy8+LkpPZiz6RSJWSX0burPxhPnefz7A7QN8+S5fk0wmlWmrTzGXdGhTLnMnYD2nc3j+eWHaRXqwZwxba+p90xhmZFxX+0lIbOEp3s3YkirIBoHXNqJ8N8QQlBx4gRFa9fiPnAgjlFRFCxbjl1oKC5du1S/N/3p0zg0ikTR2VG2Zw9O7drh3q8fYsYM0qdO5fx776GWlaPz80PrZhmbQ6Tl2oGKU6dw7tDhouMaz52jLCYGtaICzxHD//G4s2a+T0VCAg3Xr6dw1S943HknHndabtziPXEi2bNmUfrHHwCUbN1KvU8/wS4kBP2ZM+h8fNB6eFC0/jfsAgNwatOGjGnTKPxpBQCOzZvj++ikSz8rsxljRgYIgV1oqLw4yMpsNeAfyT7CG7veILEgkRDXEI7kHOGnUz/RI7QHT7R5gubezf/VvwW9Wc+Wc1voEdqDxScX8/GBj6uf83Xy5Y1db3Cm8Azu9u642bsR5hZG24C25JTnYFJNV10VuFZ1IuAPjxxOc5/mNPa6dELRxUHHy4P/rN+/MrgZPx1I5b8b4hn31V4AvJztmLPpFIVlBiZ2CeenA6nsO5vHuyNbsjU+mxlr4/B0trQSDvFy4qGuEfi6OtQY+A0mlTVH0vFxdaB1qMdFt/YrLDcy/mtLsP9ifDS9mvpf9XssP3QIoQqc27W9+HjnzpH3zTcU/77Jks2azQDkL/4Bp9atKduzBwDHVq2o98n/0Pr6ok9MxH3QQLTuHpTt2YNbn94AeNwxlPKDB8hfthy7wEDsGzasPo4uMBCNq2uNGX750WMAVBw7hiE1FfvQ0OrnTPn5aBwc0Dg71/i+1PJyyk+cAKORtGeeQS0txWPEiOrnve4dS+Evv+DcoT2u3buT/tLLpDwxmYBXXyHloYfxGD6cwOlvkDFtGo4tWxA2fz7F69bjPmQIxowMCletwmfSI5f8Eme+9RYFS5YCEPjWm3jdfffV/SCkq+Kss/y8y4y2U8NfHLeYmftn4ufkx6yes+gd1hshBItPLmbuwbmMWTOG5j7Nmd55+iVzfn9VYapg0YlFbEzeyJNtn2TFqRVsOrcJPyc/ssuz6V+/P73CeuHr5Etb/7Y8t/U5vjn+zUX70CpazMKMgsKbXd9keOQ/T5b+qToR8IEag31NtBqF0e3rMax1MCsPplFQbmRi53DeXRfHwt3JLNydDICrg47Bc3ZgMKkMbhnI+6Na89bqE3yx7QxfbDuDt4s9nRp4o6Dg7+5A32YB+Ls58J/VJ9iRmFN9PB8Xe6YOaELvpv48/G0McRlFfD7u6oO9MJvJ/t//yP38CwC8H3wAx6bN0AX4Y+fvT9K48ajFxbj26oV9/frYBQfj3KEDac89R9nevfi/+CJaN1cyZ7xL8v0PEPLB+6iFhThENsKxWVPyFy/GbcCA6uN5P/gQ+UuXYUxNxbVH9+rHFUXBoVEj9AmnUCsqMBcWYhcQAFgCPTodmEwU/7ahegmnEIKkMWNw7tCB4LffrvH9lR89CkYjGmdnyg8erBz/n/V6rZsbDX9dU/19yH8/JGXSo5y7/wFQVcpiYzGcOYNaUkLF0WPoT59GLSvDpVs3hMlI5muvU7p9O8aMDJzatsOxSWOEwUDR2nW4dOmCMT2dwlWrZMC3Mq1Gi5POiRJjyUWP51XkkZCfQD23eoS4hlzm1dYlhGBW7CwWHF9Ar3q9mNFtBq72rpYnFRjffDzDGg7jt6Tf+Pzw54z9dSwPtXyIR1s9ir32z4QtryKP+Ufns+b0Ggr0BQgE3o7eTN40GYTg4dDR/FF2hHpu9Xin2zsXXXA2t/dcSo2l2GntKDYUE58Xz/7M/QS6BLL53GZe3/k6wHUP+nUi4FecOEHu/Pk4tWmDS5cu1eUHQ0oK2bNm4dyhA+5Dh1aXJ8ByF6ExHcOqv3/zzhbc3yWcrfHZtK7nSaCHI88vPUirep68OLAZGo3COyNa0D8qgPSCcg6cKyA2OR+dVuH3uHIW7EwCQKdReHdkS8K8nTmRXsSmk+d5acVR3Bx1mMyCz+6Lpk+zgKt6X2pZGWnPPU/Jli2WrFejkPfV139uoNWidXMjYsVPOFyQjQOEL/4eY0YmDg0sp4p2YWGkPPoYZ0ffA4BDZEOco6NpErP/otfZh4bgPmAARWvXYh/R4KLnHCIjKd64kYxXp1GyfTuRGzeg9fSk/NhRnKKiEEYjRevW4T1xAopOR8Wx4xiTz1Guu/QGGVXKDxwAwO/55zj/5lt4DL8TRXP5qSXXHj3wnTyZvEWLcO3enaJff6Vk+3bL51VSQtEvvwDg1LIFuoAAzr/9DimPPlb9evfBg/G4cxhqURFe48ehj48ne/bHGDMzsQuUvYisqZ5bPdacWcNdje4iszSTRXGL2JG2AwB7jT1Pt3uacc3GodXU3FP/WhnMBtJL0lkav5Tv4r7jnib38HLHl2s8noeDB6ObjGZA+ADe3/8+847MY3n8clztXbmr0V30CuvFg+sfJF+fT7/6/Qh3D6dzcGeauUXy24wnCN9+GsesH5gwfTqe94y2nFGa9KCawN7Fcge5yj8yDk4O+Ib4Vi8GGR45nClbp+Covf5XJF/39siKogwEPga0wHwhxHuX2/bftkcu3rqVzDffxJSeAYBD06a4DxxA/pKlmHJywGTCLjiY+ou/v6pf6or4BPIWLKD4998Rej32DRsSMusjHCIiEAYDir09ank5Zfv24RQdTYWdI/uT8igoKCEyyIMWYX9eGWsyq7y55gSxyfl8NLpNjXcO+iu1vJySrVvJnjMXQ3IyAa++gvd99wFgSE5GmM1UHDtG8abN+DzyCE4toq7qc9KfPk3WR7MoP3yYhr+uQevhUfP7j4sjedx4wr5diFPUn/vO+3YR52fMqP7e98kn8X38MeI7dMRz5EjsQkPIem8mWm9vAqe9SkVCguXMRKOhSWwMGqc/L7ksP3oMUVFOzrwvMWVmEvHzCvK//x6PO+9E6+l5xfcijEbKDh7k3ISJ6IKDMJ3PArMZrYcHwmik8f59KFot2XP/hz4hAe8HH6B4w0byFizALjgYc1ERjXbtxJSRwekBA/F/4QV8Hnzgqj5H6eqcKTzDA+sfIK8iDwBvR2/uaXIPrf1asyR+CVtTttLWvy1ToqcQ6RmJm/0/m8tShUqRvghPR89LHl99ejWzYmeRW2GZtL+36b281PGlq67Pb0/dzm9Jv5FRmsH+zP04aB1wtXPli35f0MS7CcbzWRRv2ED+kiUYTp+2nFHq9ZTFxhIyaxbu/fpi/mIwIjsR3QM/wMFFkHkUOk4CvybgFgTuQZC4CU6uQXR9BsWr/j96/xe62vbI1zXgK4qiBRKAfkAqsB8YK4Q4UdP219oP35iRQfHG3yn8dQ0Vh4+g9fIibMHXmAuLSJ08GZ2fH+6DBuLQrBnu/fpd8nphNpP1/vvkLfoOjbMzbv36ofPxpmDFzyh2djg0bEDp7j249e1LRUI8xuRzaNzccOncGaHXU7pvH1pXV0I+ng2KgsbZGccmTa56/KpeT9qUZynZuhVUFfuGDQl4+WVcu3W94mutraYuh6V79nDu/gfQenjg0LwZFSfiCPvic5LGjCXovXfxGDaMks2byfnscwxJSWg9PTHl5CD0esKXLkGoavVncnrAQAwpKShaLR4jRxL0n+lXHlRpLuiLwMkTHD1RKyqIb98BzGZcunal/OhR1KIinDt0oP6iby99T6pK8vgJlMfG4nHnMIJnzgTg7F2jQFGI+HG5NT466QIJ+QksjltM15Cu9AjtgYPWcqc1IQSrz6zmvb3vUWwsRkGhS3AXHmr5EB0CLYsCyoxlfHP8GwZFDLpkUrPYUMzU7VPZm7GXz/p+RqegTqhCZdO5TXx66FMSCxJp5duKe5reQ7BLMNEB0VcV7FW9nvzvF+Paq6clwROCTw9/yrqz65jdczaRXpGU7t5N6lNPo5aUYB/ZkIAXX8S1e3fU0lKS73+AiqNHcWlZn/K4sygahXq351Cc6oTB4E5gq3R0Dipo7KD1GDi8BFQj6JxgwNvQ4eF/9TnbSsDvDEwXQgyo/P5lACHEuzVtb80boBhSUlDsHbALsNTKy2JiSH3yKcwFBQAEvPIK3hPGA6CWliJUlawPPqRg2TI8x9yD/zPPVGeaFfHxnJswETQaXPv0pnj9b2g9PPB96klKtm1Dn3AKRaPgFB1N6c5dGFNSLIPQaAh6552rWrkiTCbSnn2O4g0b8H7gAVw63YZLt24o2utzuvtvmPLzSezVG/9nn8WpTWuSRt+D1sMDc2EhDX5dU11WMpw7x5lhdyIqKvCeOJG8hQsJmDaNnM8+Q+vpSejcuZwZPBittzfmvDyCP3gfjzvuuPhgqhkUDSgKCAHrXoB98/583iMMRn/D2adnUHH8BL6Tn6D88BFKd+zA+8EHCXih5ou19GfOkvLIIwS/9271aqOcL78k+78fEbl1iyzr3GB5FXkczDrIidwTrExcSV5FHp/0+YQmXk14esvTHMk+QoBzAN8N/o5Al0AMZgNbU7Yy9+BcUotT8Xf2p8hQxKjGo9idvpv4/HjC3cN5vPXjDIwYiEapuTxozMwkY9prODRujOfIEThERmJITSX16afRn4jDLjiY8B+Xo/P2xlxSQnlsLBpXV8tZ4vff4xARTsisWdXl4yrqkVXkzJ1N/t4MXBu6U5bvhCmz8s5fOh06bw/CXn8Yh8IdcHQ5hLSHYXNg20xoPhxajPxXn6OtBPxRwEAhxMOV348HbhNCPFnT9jfijlfCaLQE1o0bCXjN0k8m5aGHq9d3+0yahP+zUy55nbmgAMXeHo2zM8JgAJ2uxlqzuaCAvMWLsa8XRuHPKyjdtRvXvn1wHzgI544dsPP3x1xSQvqLL+Ha83Y8R4wg++OPKVjxM+bcXAJefgnviROv62dwLcwlJWhdLbXIwl9/JX/xD4jycsKXL7voj1Pu1wvInj2bhuvXcWb4CLRenhiTzwGWOnrR2rU0WLsWfUI8bn37ougumE5KPwjf3QVleeAZBiHt4PjP0HYchHWB8jxL8C/NITPGlfw4HfVG+VMumpDz0x+EfPRf3AcOhKI08KhcMZQdD6ZyKMmConSIGg5OlgvI9ImJnBl6B4HTp+M15p4b80FKlygyFHH/+vtJLkzGLMxoFA1Ptn2SeUfmIYTAUedIfkU+AkGERwTTbptGmHsY96+/n6yyLCI9IxnffDyDIwZX1+mNWVlkzXyf0t27sQsNJeSD99H5+ZE0bhyG02cQZjMoCj4PP0TB0mUIkwnfRx8le84cHBo2xP2OO8hb9G11uRitFo+hQwl49RW07u6XvokFgyHjCER0h0EzMZZqyXz3XTyHD0cXEEjK44+jcXQkfNlSdPpU8IkE+5pXsP0TthLw7wYG/CXgdxRCPHXBNpOASQBhYWHRycnJ1208VYTBQOqUZynZtAmNszNaP1+8Ro9G6+mFx8gRVluTrer15MydS8GqVZizLSt33AYNRC0uoXSHZfLKsXUrKg4fwa1fPzxGjMCtdy+rHNsWmAsL0Xp4kDxhImX79qFxdUUYjQi9HofGjWnwyypI2Qcn14CLP3R63BKk5/cFrT20Hgspe+DsdmgxCv6/vfOOj6pY+/h3kk0PkEYCaYTqBSw0BUXxqtgQQbEhKiIgggp6UV9A7/taUMCK9ypelQuCHQQLYgGlKCiggHSUACEkhIRAet1sdt4/ng1JIA1Idpdkvp/PfrI7e/ac58w5+Z1nnnlmZshsKHvI5qbBl2MpOAypy9Jo0z8T6+F0UtYF02awFxYfK+SnS0sAIPtgZeNanQfDl4B/CLooh3033IRPx3NocdNN5Hz7LVH/ep3SrCzyVq6kxWUXoJqHg28V/+CGeuVIwRFeWP8CbVu05TGixRYAABuoSURBVIZ2N9AxuCPb0rfxVbwM6ovJttBl01GCf9xM0JAhtJwwgVJ7KRp90hoW9qIiEu8ZTnF8PM369yd/zRrsRUUoiwV7YSHRb83C77zzSJk0mfy1a/GKjibm3XfxadeWnOXLSXv+BWxHjuDdpg3hkyaBh8K3Uye8IiOrP4F3+kl8ftiCKr8u3L6dxLvvwbN5czyDgtA2G7q0FG0rIfiOoVWOGakL7iL4Lgvp1EaZ6Bdu20rchx8en0OmQY5VWkrRzp3krlhJxty56JISIp6cQu7KVRSsX0/45EmEjhjRYMd3NWnTZ5Axfz4tbr0FXVhEzjffSEuqXwtY+g9QnqBLIfZiSHN074xaBuGOXOicwxAYDjVlc9hL4Wi8PCD2/igPjMgecEAGbPG3GyCgJfgGQcExWDQSfJqJ8CdtIHWDN1kJzVCeHtiLSmj/zRdkLfmeY++8Q0BkCdGP3oLHoBcbtqKaOMX791Ny6BABffqgvCSzqyQlhcPPPkv+mrV4+Plhz88HpfCOi8OakEDradMIGlI+bqOs70lrTcpjj5Hz3fdEv/FvmvXvT0lKCkdnz0Z5eBLQt+9x50qXlpK7fDn+vXtjCQmptK+SpCQsERF4+PjU7STe6AmtL4Bb51a7Sd6atWQu+BSlPFBeFvC0oCwWAi/vJy3T08BdBN+CdNpeBRxCOm2Haa13VrW9sxcx11pDSQnKiXO9FyckYN23j2b9+2MvLsaamIhvJzedgdJuB7QIbalNRNniA9YC6TxtVkO8226H/CMQGEH2G5NIeetrYqc9gorpSeJ9I2n70nh8N06BdlfIP8fWT+D7yfL5+hchrGPDnlviOtg4B9J2QnQv8rYlkPThPlAatCJqYBBZhyIp3LUbu9VOcJ8oWr23omFtasIU7d5N4r0jsOfk4BkcTNAdt+Ph58+xd95BA0G33gKldnw6diCwXz8sLVtycMwYCtatx6dLZ0KGDcOvWzeSx0/AMzQE385dyPzgA1o+NpGw++933om8cg50ugYGveG8Y+Imgu8wZADwOpKWOVdr/UJ12zpb8A3VYM2HVdNg+yIoyhZP+2i8CH6Hq+DAWijMFI88OA68/CGii3jiOYcgsrt0SCX/Dn7B6PxMCrJaEBCSA1E9sWek4FGYAmGdYPSP4OtIDy3KEa/bBdMc2IuLSbztJoJuGkjqa/8htFMumfG+NI8txFbkTXFhIB1+3eJ0uxorRXv2kDFnLkF33I69oJCUJ55A+foSPnEiOcu+J2/FStCawCuvJOLJKZVGb5dRmpdP1sKF5CxdStGuXaAUnsHBaJsNe04OLQYPpvWM6c6dNmNaNPS4B66rMojRYLiN4J8KRvAbiB2fw87PYeC/ICAUspNh41xoHgU97pUQSO5h6SCN7gWL74e9P8A5A6TTM3UHtOwEpSXw13cQ01uarbu/huJseSgUZUtoxs8RMgkIlxSzY3shshv0GA4rpsKRXRKe6XA1dB4oAu9m7B8yBHt2FiWHDtNq4ij05s9IW51Dh5Urao7fGmrEmnyIjHnzUF5eZC1YUGkiPO/27Yl5a1b59NwHDlCal1+nMSZaa3KWLCF39WrCH3scZfEkd+VKgm691bkrtWkNzwZDv8fhyn8677jUXfAbxUhbQzVYC2D9W7ByqnzOOihx7T8+lBGAaFjxrIh1GcoDtB1ueA0uHFXz/v8+Sf5qDdlJ4B8q3n7GfgiMAJ/AytsPeKneTq0h8evalazPFsn7fgMh+wdYnUP+uvUE3VJz2pzdaiVj/nx0YRFhD447nn1U1biGpoQtM5OkUaOwpqRAaSm+551L5IwZ5Hwt02aEPjCmUpzcOy6uzvtWSlWacA8gZNiwerO9zpQUAhq83XcNaCP4jYmCDNjwjnjvykPSEIuyoctgOO82+Ow+OLJbvO1LH4WD62HHYjj3FgnPHIuH+B8gpF3tYl8RpaR1UEZo++q3PQvw7doVPluE8vWVPOuoYDz9ksjfUFnwrQcOoLy9j3v9+evXk/rsc1gTEgAo+P13vCIjKdq9m5LkZMIeepDQUVKv9sJCbMcyKq0y1lixZWaSdP8YSlJTaTN/Hn7nnSdpzUrRcsL42ndwtmB1zAzqHVjzdi7ECP7ZSM5hySVvfQF4WiTUsvUT+PEZCad0ug78wwAN3YZBm74iymPXSJZK89ayn6BYOP/28v0GxUD7K11xRm6Fr2M6Cd8uXcRD9wkkIEpRsG492mZDWSzk/fQTyY88ivLyIuKpJ8lb/RO533+PV2wsMbPfxXYkndTnn6f4QAK+XbrgGRzMkZdfwV5YhCW8JUf/8za29HRi3n6bwEv7om02ivfuxSs6+vg4h8ZASdoRDo4aScnBJKJefx3/Hj1cbVLDYXVMFGcE33BaJKyBxF8l2yW8s3jex/bBD09DST74tICIrhJOyU6S2Po9X0Lr86veX3jNU74aBJ9OnVB+fvh17yYFXv4ExtjI2ZvOvuuux9IqgsI/tuBzTicosXF48hSUvz9hDz9M6OhRePjKJFgtbhp8fDCatlpJevhhjr75phyjY0c8AwNJnjABvwvOp3DrNnRBAZbwcCJnTCfgkkuO22PLzKRg40Zs6em0GDCgTnMNuQOl2dkkjR6NLeUwMbNnE9D7Ileb1LAc9/BNSMc5FOdJCqGXX+3buis2K+xbARvehv2rpcynuaRBlhF3mYRlEn+F9D9ltN6Al6HjteUDkwynjYePD20/W4ilbJoF70CaR+fh8dYHZMx/X6aMGD6csIceBCBn6VKa9e+PJazyesQVRx4rb29i3n4b64FEtK0En3btsB3LIHnCeEqzsgm6+WZ8/nYOGe/N4+Co0US+9BLNB95AztKlpD43FXtuLgBHZ71Fq6f/j+bXXOOcyjhNSvPySRr3IMUHDhD77juNX+zhrBD8xpGlk7AGVj4vaYAeFkkLtBVCXrp4wkM/hrhL69/g+iQvHVZPkykECjMlJHPZROh5nzzAshIllOPhKfNvGGF3Hiufh59fgaczGzxl1F5YSNLYcRRs3IhXVBQlBw/i17074U88jvL0JHXq8xTt2EHIiBGEP/5Y5Skp3ITSnByS7h9D4Y4dRL36Ks2vu7b2HzUG9q6AD4fAyOUQ29uph25aWTqe3lBqlY5IWzEkb5S0wIhz5WHw1cOS7717CWQmykOh9fnQupvEsV2dPWGzwoK7ZA6ZzoMkrt7+SvCsMI98cJy8DM7HOwDQkoVRD/Oe1ISHnx8xb80i+R//QFtLCBs7lhaDBx1vLcR9/BFpL71Mxrx5FO/bR9TMmXgGuo9HWbRrF8mP/oOSlBSiZr7m9i2ReuUs8PAbh+DH9oYxq6r+LuFnmH8jvNYFSotlWlK0Iy0RsPhKJ4suFfEf+gm0iJKn9bInZTh+15th30rJPrl8UvkFLciQcItnNdWotTx8wjufnKIIUJwLe5aJV5+0AW5977RnyzM0IGWdcCUFDS74AB4BAcS++26V3ylvb1r98yl8OnUk9dnn2HPhhTJg6fHHjq+Z4CoKt2wh8b6ReDZvTpv35zfuDtqqMILvBrTtB5f+Aw5thiuelI5NWzEc2QkpWyAzQWL/ygO2LYQPboLgthC/DELaywjTbyZKi+HPpbDzSxg4E/avgl/fkNaFf5hc5P7PyGAigPyjMk/M7iUymdK1L0DXIbDrSxnBGtIOti2AvDQZsPT3KUbs3RUvh8hb8yAgrOZtnUTw7bfj064deWvWUrR9G2lTn8eel3/ak2+dKUV79pD0wFgsLVsS99GHWFq2dIkdLsVk6bgJ/Z+p/NnLF6J6yqsi5w6RaXkLM+GKf8Il4wENR/dIeOjgelgyXuJ0ILM5BrSU6XpTtkpY5oI7Jea+dYEjzDRROmEXjYTf50hHq38o/PmNTPt72zyJyVucOCLQcGqUeWxW91mQG8C/Vy/8e/VCl5SQMuVJ0mfOxMPPl5Dhw095X7q0lNSpU0FD+GMTq576FxH2gt9/xxIaRuDfL8fD15fC7TtIuv9+lLc3sXP+2zTFHioIvvHwzw7iLoVHt0uYxqvC+pKtL3B83xfG/SKefUBL6DmiPP5fUgjfPg67l0rTrssgCf+0PEeGWa95DVZPh07Xwm3zAS3hJFf3Hxhq57jg57vWjmpQXl5EzpiOLi4ibdp0lI8vwXfcXvsPHWitSXthGlmfLgClyFu1itZTnyPw8ssrbZO9eDGpz02V9SCAgL59CR01UiYsCwoi9r25eMfE1Pv5nTVY8wHl1lmCRvBPJDC85u+9/ODy/6m6fPAseWldWcg9POHyJ+QB4R9qMmzONsoEv8Q9BR9AWSxEvvoqyePHk/r00+ji4uMrulWHvagIe0EBaTNmkLPka0Luu4/mAwZw+MkpJD0wFq/YWHRREYFXXYktNY28VasIuORiWj3zDHlr15L23FTyf/kF7/btiZ07B6+ICCedrZtizZdwjhs7cUbwG4LqLnhgE23qnu0cj+G7r+ADeHh7E/3GG6Q89hhp06ZhLyzEOy6O4vh4fDp1pGjHTor37iXg4ovJX7eOvJUr5YeenoSNf5iwceNQHh7ELV5Mxpw5FP21B4DsxZ+DhwfhkyYRMvwelKenzFVjs5H3yy9ETp9eaR75Jos1r+rkDDfCCL7BUBtlnXBuLvggoh/12mukTJpM+syZlb/09MQSEU7eypV4NGtG6OhReAYF4d+7D37nnVtpH2Hjxh3/XJqVhdYaS3Bwpd2FDB9+Wv0FjRZrvlvH78EIvsFQO24ewz8R5eVF5Msv4de9O14x0QRceCHF8fEyT09oKNYDB7CEhlbbMXsiZ8tUDi7HCL7B0AjwPjtCOhVRnp6E3HP38c9+3bodf+/Ttq0rTGr8lMXw3RjTe2gw1IZXWaete6VlGtwMa57be/hG8A2G2vC0gKdPeZ61wVAVZ0FIxwi+wVAXvAPcbuCVwc0wgm8wNBK8A86qGL7BBVjzTAzfYGgUeAe49cArg4vRWubkMh6+wdAIMB6+oSZsxTLjrvHwDYZGgJe/ieEbqucsWMAcjOAbDHXDO9Bk6Riq5yyYKRPMwCuDoW54+5s8fMPJaA2LR8u6FmAE32BoFJgY/tnHibPW1hf5R2XBJN8g2L4Qdiwq/87NQzpG8A2GuuBVSx5+ca4IjG/d5qcx1DP2UkjbCa3OE5G322X1Onsp3PEB+IeUb5d7WITZzzFHkK0Ydn8NHfqXl1XHtoXw+f3yvkWMXPfoi2StjLUz3X5GXCP4BkNd8A6QOG1VXmPeEZhztTwQ7vwUontWvQ9D/WCzilfdpi8Et5Gyn16Cn2ZA93tgwCuyTnTCT4CC/14lixtlHZRV62xFENQGxv0qD4kvx0HGPuh2F9z0Vs3H/m22LIF64WjYvxpS/oAbX4fwLnLskHYNffZnhBF8g6EuePtL2p2tuHw1tKUTIXWbeHm5abIK2rwBMGp5+SpphprJTARth5A6Tui2bxV89z+y7GjHa+GuhVCQAetmQVAs/PEB7P9J+luiesnypsumwJ7lsvhQz/tkXeKVz8MXD8i2AaHwt4Gw5WPo8yC0OrfyMbWGoizITYXk3+CaF+CSh+VVkdD29VEjDcoZCb5S6jbgGaAzcJHWemOF76YAo4BSYILWetmZHMtgcCllsdn8IyIsOSmw6T0IjBDBv22erJE86yIRk7s+k/DBvlWy4ln7K+rHjsIsaWH4tqif/TU0yZvg13/DDa+evAD8gbXwyZ1QWiKeddeby1tPmYnikbe9TD4XZMA3E8VzD24LnW+U5UQz9sPmD6T1NWq5hGt+fhmSN8L1L0J0Lxi79mS7clPh99nQLBJGfCsP9ANr4aNbpQVQnAfNWsE1U2HLJ7D3R1mu1MMLLhjasHXWgJyph78DGAK8U7FQKdUFGAp0BSKBH5VSnbTWpWd4PIPBNQTFyt83esGVT4lXqu0w4pvKnt0l42HFs7KG8ab3RLQ8vWHCFmgRBYWZ8Old4mmefxsc2gzBceUxZpAHxbdPyH4vfkjKSktgzauynnJQLDywRiZ1s5fKA6UmtIZN8yDmIojoWp+1UjPWAvh8tIhycQ7ctajc1oSf4cNbJSTjFwyL7pM1obsNg6unwoK7pfU06E3waQbfT4H8dLjiKbhkgtTjX9/BF2Ph0CY4dwhEdJFXh6tkjema1pbt/zR4WKDnvXJdAK6bDr//FyLOleuxZxl8NkK269Af4pdB1yEnP7jOIs5I8LXWuwHUyT3hg4FPtdbFQIJSai9wEbDuTI5nMLiMc66HMath9Yvww9MSHojpfXIz/qIxIsornpW47sDXRcjWzoQbXoFlT0HiLxL7zUqU1kDzKBi2oDyUsGoabJwj732aQ497YPV0EfyYPpC0Hn57V7zOhJ8grJMIY1V9B1pLCOS3d8Xmuz+Ho/EQ2R3COjRkjcHqaSL23e+RUMuikdB7rHjQC++VMM6Ib2VZwD8+hPgfpO6s+SL2LWJgiSNsEnYODPu0PFTm1Ro6D4Kdn0PcZVLPFaltIXGfZnD9jMpl3YbJq4zLJ8sDIKY3xFwo9VbbmtdujtJan/lOlFoNPF4W0lFKvQms11p/6Pg8B/hOa72oit+OAcYAxMbG9kxMTDxjewyGBsNaALOvgPQ/RWR63XfyNnuWw9G/4KIHwOINXz8i8eGeI0R4ewyHXV9BUbaISVaS5HFH9ZB0v6QN0O1uCU/sXw3d7pTfXzAMBr8J7w2Ag78CSvYZv1w857Fry0M9BRmw6gUJKWXsk/3t+R4Kjsr3LWJgjCN+XRUJP0vroWIoKv8YHFwnr/S/oM84iL4QvpsktvcaCXYbWHwklDOnv5zrwNfFlnWzyscyBLaC0T9CUEz5/ktL4J1+cGSXdKqOXQM/vyL77jzo5JZMVhLsWCwPkbJ+lSaKUmqT1rpXrdvVJvhKqR+BVlV89ZTW+ivHNqupLPizgHUnCP63WuvFNR2rV69eeuPGjTVtYjC4nqPx4vld+b91W7Q66yDM6gOlxeKNDlsACWtErAa8JB7tb7PhwBqJEUf1gCv/CaVW+H6yeL/No+HBX0XQU/6QcEj/p0VQk36DuddBaAeJRTdrDSlbJATSoT90vFrEOHU7bP0EWp0vD6HY3jD0Y/F2j8bD9kWS1tjhKph5LqBh4m4R8H0r4eOhcg6e3mJHca7EtQ9vlfP0bgbWXMmeyT8q3z+0vvwhVJQtDxJtlwddsypk5eAGeH8w3PgvuOCOertkjZ16E/w6Hmw1lQV/CoDWerrj8zLgGa11jSEdI/iGRktJoQhlbfH2qkjdLqIcHFdedmJ66KZ5sPl9GQyUkyLCf8NrENntxL0JWz6GLx8UTz8gDFI2S7mXv3jua16Vz0NmQ3hnmHu9xNsHvCLhIGuePGQy9sGtcwEF+1fJ8TfNk6yWYQuh07Wnfr7WgvJlJQ11wtWC3xX4GInbRwIrgI61ddoawTcYnMjBDfDdE/LwOP8OiL0Y5t8o00C37iYeuqeXeOsWHwnBNI8s/31BBmQnQ+vzK+83Lx3Sd0Pbfs49nyZMXQX/TNMybwbeAFoC3yiltmitr9Va71RKLQR2ATbgIZOhYzC4GbG94YGfK5dd8SQsfwr6PiJ9CMuelNTFe7+uLPYgmSwVs4vKCGzp9iNOmyr14uHXF8bDNxhcjNbSaRrRVTz8n1+WjmE3H0Ha1HGKh28wGBoZSpXn6vs0g6ufc609hnrFzIdvMBgMTQQj+AaDwdBEMIJvMBgMTQQj+AaDwdBEMIJvMBgMTQQj+AaDwdBEMIJvMBgMTQQj+AaDwdBEcKuRtkqpdOB050cOA47Wozn1ibvaZuw6NdzVLnBf24xdp8bp2tVGa13rfBZuJfhnglJqY12GFrsCd7XN2HVquKtd4L62GbtOjYa2y4R0DAaDoYlgBN9gMBiaCI1J8N91tQE14K62GbtODXe1C9zXNmPXqdGgdjWaGL7BYDAYaqYxefgGg8FgqIFGIfhKqeuUUn8ppfYqpSa70I4YpdQqpdRupdROpdQjjvJnlFKHlFJbHK8BLrDtgFJqu+P4ZUtRhiilflBKxTv+BrvArnMq1MsWpVSOUupRV9SZUmquUuqIUmpHhbIq60gJ/3bcc9uUUj2cbNfLSqk/Hcf+QikV5CiPU0oVVqi3t51sV7XXTSk1xVFffymlTmOx2zO2bUEFuw4opbY4yp1ZZ9VphHPuM631Wf0CPIF9QDvAG9gKdHGRLa2BHo73zYA9QBfgGWTNX1fW0wEg7ISyl4DJjveTgRfd4FqmAm1cUWdAP6AHsKO2OgIGAN8BCugDbHCyXdcAFsf7FyvYFVdxOxfUV5XXzfF/sBXwAdo6/mc9nWnbCd+/CvyfC+qsOo1wyn3WGDz8i4C9Wuv9Wmsr8Ckw2BWGaK0Pa603O97nAruBKFfYUkcGA/Md7+cDN7nQFoCrgH1a69MdfHdGaK1/BjJOKK6ujgYD72thPRCklGrtLLu01su11jbHx/VAdEMc+1TtqoHBwKda62KtdQKwF/nfdbptSikF3A580lDHr44aNMIp91ljEPwoIKnC52TcQGSVUnFAd2CDo+hhR5NsritCJ4AGliulNimlxjjKIrTWh0FuRCDcBXZVZCiV/wldXWdQfR250303EvECy2irlPpDKfWTUuoyF9hT1XVzp/q6DEjTWsdXKHN6nZ2gEU65zxqD4KsqylyaeqSUCgQWA49qrXOA/wDtgW7AYaQ56Wz6aq17ANcDDyml+rnAhmpRSnkDg4DPHEXuUGc14Rb3nVLqKcAGfOQoOgzEaq27AxOBj5VSzZ1oUnXXzS3qy8GdVHYsnF5nVWhEtZtWUXba9dYYBD8ZiKnwORpIcZEtKKW8kAv5kdb6cwCtdZrWulRrbQdm04BN2erQWqc4/h4BvnDYkFbWPHT8PeJsuypwPbBZa50G7lFnDqqrI5ffd0qpe4GBwF3aEfB1hEyOOd5vQmLlnZxlUw3XzeX1BaCUsgBDgAVlZc6us6o0AifdZ41B8H8HOiql2jq8xKHAElcY4ogNzgF2a61fq1BeMeZ2M7DjxN82sF0BSqlmZe+RDr8dSD3d69jsXuArZ9p1ApW8LlfXWQWqq6MlwHBHFkUfILusSe4MlFLXAZOAQVrrggrlLZVSno737YCOwH4n2lXddVsCDFVK+Sil2jrs+s1ZdlWgP/Cn1jq5rMCZdVadRuCs+8wZPdMN/UJ6svcgT+anXGjHpUhzaxuwxfEaAHwAbHeULwFaO9mudkiGxFZgZ1kdAaHACiDe8TfERfXmDxwDWlQoc3qdIQ+cw0AJ4lmNqq6OkKb2LMc9tx3o5WS79iKx3bL77G3Htrc4rvFWYDNwo5Ptqva6AU856usv4HpnX0tH+Txg7AnbOrPOqtMIp9xnZqStwWAwNBEaQ0jHYDAYDHXACL7BYDA0EYzgGwwGQxPBCL7BYDA0EYzgGwwGQxPBCL7BYDA0EYzgGwwGQxPBCL7BYDA0Ef4ftORmdRPptfEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.060530</td>\n",
       "      <td>-2.887943</td>\n",
       "      <td>2.419412</td>\n",
       "      <td>0.908175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.773117</td>\n",
       "      <td>-3.003151</td>\n",
       "      <td>2.359755</td>\n",
       "      <td>0.756587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.816198</td>\n",
       "      <td>-2.953174</td>\n",
       "      <td>2.774531</td>\n",
       "      <td>0.618809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.350169</td>\n",
       "      <td>-2.929736</td>\n",
       "      <td>2.893389</td>\n",
       "      <td>1.019772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.085558</td>\n",
       "      <td>-3.566645</td>\n",
       "      <td>3.299516</td>\n",
       "      <td>0.859867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.177671</td>\n",
       "      <td>-3.340701</td>\n",
       "      <td>3.712991</td>\n",
       "      <td>0.423561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.012698</td>\n",
       "      <td>-3.302543</td>\n",
       "      <td>3.611960</td>\n",
       "      <td>0.424597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.783180</td>\n",
       "      <td>-3.384022</td>\n",
       "      <td>3.363775</td>\n",
       "      <td>0.011063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.590633</td>\n",
       "      <td>-3.465373</td>\n",
       "      <td>3.730589</td>\n",
       "      <td>-0.406819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.893002</td>\n",
       "      <td>-3.486088</td>\n",
       "      <td>3.773050</td>\n",
       "      <td>0.055843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.488150</td>\n",
       "      <td>-3.474417</td>\n",
       "      <td>4.014207</td>\n",
       "      <td>0.013911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.351290</td>\n",
       "      <td>-3.912176</td>\n",
       "      <td>3.804653</td>\n",
       "      <td>0.454455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.592446</td>\n",
       "      <td>-3.730252</td>\n",
       "      <td>3.551222</td>\n",
       "      <td>0.693068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.252394</td>\n",
       "      <td>-3.458582</td>\n",
       "      <td>3.352049</td>\n",
       "      <td>0.833815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.615042</td>\n",
       "      <td>-3.666618</td>\n",
       "      <td>2.875478</td>\n",
       "      <td>0.793399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.904937</td>\n",
       "      <td>-3.509938</td>\n",
       "      <td>3.097578</td>\n",
       "      <td>0.371861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.410280</td>\n",
       "      <td>-3.774180</td>\n",
       "      <td>3.353890</td>\n",
       "      <td>0.585673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.373855</td>\n",
       "      <td>-4.040941</td>\n",
       "      <td>3.002045</td>\n",
       "      <td>0.387076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.254923</td>\n",
       "      <td>-3.688319</td>\n",
       "      <td>3.142167</td>\n",
       "      <td>0.661533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.275424</td>\n",
       "      <td>-3.755768</td>\n",
       "      <td>3.709418</td>\n",
       "      <td>0.468495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.145376</td>\n",
       "      <td>-3.758103</td>\n",
       "      <td>3.716983</td>\n",
       "      <td>0.462192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.121012</td>\n",
       "      <td>-4.098771</td>\n",
       "      <td>2.687337</td>\n",
       "      <td>0.592780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.484584</td>\n",
       "      <td>-4.130221</td>\n",
       "      <td>3.352592</td>\n",
       "      <td>0.263948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.389605</td>\n",
       "      <td>-3.927507</td>\n",
       "      <td>3.592844</td>\n",
       "      <td>0.391410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.330106</td>\n",
       "      <td>-3.643611</td>\n",
       "      <td>3.708611</td>\n",
       "      <td>0.003234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.970960</td>\n",
       "      <td>-3.883088</td>\n",
       "      <td>4.224600</td>\n",
       "      <td>-0.142106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.291141</td>\n",
       "      <td>-4.174954</td>\n",
       "      <td>4.293993</td>\n",
       "      <td>-0.240766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.688795</td>\n",
       "      <td>-3.788701</td>\n",
       "      <td>4.616232</td>\n",
       "      <td>-0.502973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.117202</td>\n",
       "      <td>-3.886541</td>\n",
       "      <td>4.884935</td>\n",
       "      <td>-0.019758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>8.893828</td>\n",
       "      <td>-9.241530</td>\n",
       "      <td>0.611777</td>\n",
       "      <td>-1.053146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>8.664630</td>\n",
       "      <td>-8.533465</td>\n",
       "      <td>0.852345</td>\n",
       "      <td>-0.571571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>9.126901</td>\n",
       "      <td>-8.903229</td>\n",
       "      <td>0.883148</td>\n",
       "      <td>-0.075540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>9.070452</td>\n",
       "      <td>-8.926100</td>\n",
       "      <td>1.294560</td>\n",
       "      <td>0.027764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>8.710409</td>\n",
       "      <td>-8.815991</td>\n",
       "      <td>1.650591</td>\n",
       "      <td>-0.286059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>8.721622</td>\n",
       "      <td>-8.764825</td>\n",
       "      <td>1.915065</td>\n",
       "      <td>-0.252924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>8.927362</td>\n",
       "      <td>-9.398972</td>\n",
       "      <td>2.106074</td>\n",
       "      <td>0.236873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>8.746750</td>\n",
       "      <td>-8.936769</td>\n",
       "      <td>2.012135</td>\n",
       "      <td>0.095387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>8.517075</td>\n",
       "      <td>-8.692627</td>\n",
       "      <td>1.749971</td>\n",
       "      <td>-0.229970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>8.925363</td>\n",
       "      <td>-9.441839</td>\n",
       "      <td>1.598497</td>\n",
       "      <td>0.129212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>9.025887</td>\n",
       "      <td>-9.356461</td>\n",
       "      <td>1.321479</td>\n",
       "      <td>0.440794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>8.998970</td>\n",
       "      <td>-9.351177</td>\n",
       "      <td>1.055043</td>\n",
       "      <td>-0.058375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>9.187786</td>\n",
       "      <td>-9.634353</td>\n",
       "      <td>1.056138</td>\n",
       "      <td>-0.172806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>9.180293</td>\n",
       "      <td>-9.308917</td>\n",
       "      <td>0.697511</td>\n",
       "      <td>0.108731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>9.524613</td>\n",
       "      <td>-9.561295</td>\n",
       "      <td>1.119343</td>\n",
       "      <td>0.066564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>9.633443</td>\n",
       "      <td>-8.515248</td>\n",
       "      <td>1.336723</td>\n",
       "      <td>0.255515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>9.522356</td>\n",
       "      <td>-8.529564</td>\n",
       "      <td>1.367033</td>\n",
       "      <td>0.612731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>9.960627</td>\n",
       "      <td>-8.678614</td>\n",
       "      <td>1.596895</td>\n",
       "      <td>0.735369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>9.735163</td>\n",
       "      <td>-8.023111</td>\n",
       "      <td>1.288135</td>\n",
       "      <td>0.999685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>9.662709</td>\n",
       "      <td>-8.475431</td>\n",
       "      <td>1.741386</td>\n",
       "      <td>1.132604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>41.646474</td>\n",
       "      <td>0.184893</td>\n",
       "      <td>1.485044</td>\n",
       "      <td>0.670088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>10.334505</td>\n",
       "      <td>0.230987</td>\n",
       "      <td>1.972141</td>\n",
       "      <td>0.412289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>10.133042</td>\n",
       "      <td>0.871281</td>\n",
       "      <td>1.913733</td>\n",
       "      <td>0.614529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>10.258895</td>\n",
       "      <td>0.486600</td>\n",
       "      <td>2.151388</td>\n",
       "      <td>0.213664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>9.792009</td>\n",
       "      <td>0.800073</td>\n",
       "      <td>1.742501</td>\n",
       "      <td>0.509913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>9.909111</td>\n",
       "      <td>0.612172</td>\n",
       "      <td>1.757602</td>\n",
       "      <td>0.185826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>10.124215</td>\n",
       "      <td>0.609309</td>\n",
       "      <td>1.718569</td>\n",
       "      <td>-0.300031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>10.584127</td>\n",
       "      <td>0.343486</td>\n",
       "      <td>1.602878</td>\n",
       "      <td>-0.286739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>10.242992</td>\n",
       "      <td>0.743488</td>\n",
       "      <td>1.707858</td>\n",
       "      <td>-0.258555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>10.371311</td>\n",
       "      <td>0.499875</td>\n",
       "      <td>1.298326</td>\n",
       "      <td>-0.278375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            x0        x1        x2        x3\n",
       "0     3.000000 -3.000000  2.000000  1.000000\n",
       "1     3.060530 -2.887943  2.419412  0.908175\n",
       "2     2.773117 -3.003151  2.359755  0.756587\n",
       "3     2.816198 -2.953174  2.774531  0.618809\n",
       "4     2.350169 -2.929736  2.893389  1.019772\n",
       "5     2.085558 -3.566645  3.299516  0.859867\n",
       "6     2.177671 -3.340701  3.712991  0.423561\n",
       "7     2.012698 -3.302543  3.611960  0.424597\n",
       "8     1.783180 -3.384022  3.363775  0.011063\n",
       "9     1.590633 -3.465373  3.730589 -0.406819\n",
       "10    1.893002 -3.486088  3.773050  0.055843\n",
       "11    1.488150 -3.474417  4.014207  0.013911\n",
       "12    1.351290 -3.912176  3.804653  0.454455\n",
       "13    1.592446 -3.730252  3.551222  0.693068\n",
       "14    1.252394 -3.458582  3.352049  0.833815\n",
       "15    1.615042 -3.666618  2.875478  0.793399\n",
       "16    1.904937 -3.509938  3.097578  0.371861\n",
       "17    1.410280 -3.774180  3.353890  0.585673\n",
       "18    1.373855 -4.040941  3.002045  0.387076\n",
       "19    1.254923 -3.688319  3.142167  0.661533\n",
       "20    1.275424 -3.755768  3.709418  0.468495\n",
       "21    1.145376 -3.758103  3.716983  0.462192\n",
       "22    1.121012 -4.098771  2.687337  0.592780\n",
       "23    1.484584 -4.130221  3.352592  0.263948\n",
       "24    1.389605 -3.927507  3.592844  0.391410\n",
       "25    1.330106 -3.643611  3.708611  0.003234\n",
       "26    0.970960 -3.883088  4.224600 -0.142106\n",
       "27    1.291141 -4.174954  4.293993 -0.240766\n",
       "28    1.688795 -3.788701  4.616232 -0.502973\n",
       "29    2.117202 -3.886541  4.884935 -0.019758\n",
       "..         ...       ...       ...       ...\n",
       "170   8.893828 -9.241530  0.611777 -1.053146\n",
       "171   8.664630 -8.533465  0.852345 -0.571571\n",
       "172   9.126901 -8.903229  0.883148 -0.075540\n",
       "173   9.070452 -8.926100  1.294560  0.027764\n",
       "174   8.710409 -8.815991  1.650591 -0.286059\n",
       "175   8.721622 -8.764825  1.915065 -0.252924\n",
       "176   8.927362 -9.398972  2.106074  0.236873\n",
       "177   8.746750 -8.936769  2.012135  0.095387\n",
       "178   8.517075 -8.692627  1.749971 -0.229970\n",
       "179   8.925363 -9.441839  1.598497  0.129212\n",
       "180   9.025887 -9.356461  1.321479  0.440794\n",
       "181   8.998970 -9.351177  1.055043 -0.058375\n",
       "182   9.187786 -9.634353  1.056138 -0.172806\n",
       "183   9.180293 -9.308917  0.697511  0.108731\n",
       "184   9.524613 -9.561295  1.119343  0.066564\n",
       "185   9.633443 -8.515248  1.336723  0.255515\n",
       "186   9.522356 -8.529564  1.367033  0.612731\n",
       "187   9.960627 -8.678614  1.596895  0.735369\n",
       "188   9.735163 -8.023111  1.288135  0.999685\n",
       "189   9.662709 -8.475431  1.741386  1.132604\n",
       "190  41.646474  0.184893  1.485044  0.670088\n",
       "191  10.334505  0.230987  1.972141  0.412289\n",
       "192  10.133042  0.871281  1.913733  0.614529\n",
       "193  10.258895  0.486600  2.151388  0.213664\n",
       "194   9.792009  0.800073  1.742501  0.509913\n",
       "195   9.909111  0.612172  1.757602  0.185826\n",
       "196  10.124215  0.609309  1.718569 -0.300031\n",
       "197  10.584127  0.343486  1.602878 -0.286739\n",
       "198  10.242992  0.743488  1.707858 -0.258555\n",
       "199  10.371311  0.499875  1.298326 -0.278375\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = dg.add_outliers({'extreme': [{'n': 0, 'timestamps': [(50,), (190,)]}],\n",
    "                      'shift':   [{'n': 1, 'timestamps': [(100,190)]}],\n",
    "                      'trend':   [{'n': 2, 'timestamps': [(20, 150)]}],\n",
    "                      'variance':[{'n': 3, 'timestamps': [(50, 100)]}]})\n",
    "\n",
    "for col in df.columns:\n",
    "    plt.plot(df[col], label=col)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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